scholarly journals Study of groundwater contamination and drinking suitability in basaltic terrain of Maharashtra, India through PIG and multivariate statistical techniques

2020 ◽  
Vol 69 (4) ◽  
pp. 398-414 ◽  
Author(s):  
Vasant Wagh ◽  
Shrikant Mukate ◽  
Aniket Muley ◽  
Ajaykumar Kadam ◽  
Dipak Panaskar ◽  
...  
Keyword(s):  
Major Ions ◽  
Principal Component ◽  
World Health ◽  
Quality Data ◽  
Statistical Techniques ◽  
Multivariate Statistical Techniques ◽  
Multivariate Statistical ◽  
Water Quality Data ◽  
Post Monsoon ◽  
Groundwater Samples

Abstract The integration of pollution index of groundwater (PIG), multivariate statistical techniques including correlation matrix (CM), principal component analysis (PCA), cluster analysis (CA) and various ionic plots was applied to elucidate the influence of natural and anthropogenic inputs on groundwater chemistry and quality of the Kadava river basin. A total of 80 groundwater samples were collected and analysed for major ions during pre- and post-monsoon seasons of 2012. Analytical results inferred that Ca, Mg, Cl, SO4 and NO3 surpass the desirable limit (DL) and permissible limit (PL) of Bureau of Indian Standards (BIS) and the World Health Organization (WHO) in both the seasons. The elevated content of total dissolved solids (TDS), Cl, SO4, Mg, Na and NO3 is influenced by precipitation and agricultural dominance. PIG results inferred that 52.5 and 35%, 30 and 37.5%, 12.5 and 20%, 2.5 and 5% groundwater samples fall in insignificant, low, moderate and high pollution category (PC) in pre- and post-monsoon seasons, respectively. PC 1 confirms salinity controlled process due to high inputs of TDS, Ca, Mg, Na, Cl and SO4. Also, PC 2 suggests alkalinity influence by pH, CO3, HCO3 and F content. PIG and statistical techniques help to interpret the water quality data in an easier way.

scholarly journals Evaluation of significantly modified water bodies in Vojvodina by using multivariate statistical techniques

2013 ◽  
Vol 67 (5) ◽  
pp. 823-833
Author(s):  
Svetlana Vujovic ◽  
Srdjan Kolakovic ◽  
Milena Becelic-Tomin
Keyword(s):  
Water Quality ◽  
Water Bodies ◽  
Total Variance ◽  
Quality Data ◽  
Statistical Techniques ◽  
Data Sets ◽  
Multivariate Statistical Techniques ◽  
Multivariate Statistical ◽  
Water Quality Data ◽  
Cluster Properties

This paper illustrates the utility of multivariate statistical techniques for analysis and interpretation of water quality data sets and identification of pollution sources/factors with a view to get better information about the water quality and design of monitoring network for effective management of water resources. Multivariate statistical techniques, such as factor analysis (FA)/principal component analysis (PCA) and cluster analysis (CA), were applied for the evaluation of variations and for the interpretation of a water quality data set of the natural water bodies obtained during 2010 year of monitoring of 13 parameters at 33 different sites. FA/PCA attempts to explain the correlations between the observations in terms of the underlying factors, which are not directly observable. Factor analysis is applied to physico-chemical parameters of natural water bodies with the aim classification and data summation as well as segmentation of heterogeneous data sets into smaller homogeneous subsets. Factor loadings were categorized as strong and moderate corresponding to the absolute loading values of >0.75, 0.75-0.50, respectively. Four principal factors were obtained with Eigenvalues >1 summing more than 78 % of the total variance in the water data sets, which is adequate to give good prior information regarding data structure. Each factor that is significantly related to specific variables represents a different dimension of water quality. The first factor F1 accounting for 28 % of the total variance and represents the hydrochemical dimension of water quality. The second factor F2 accounting for 18% of the total variance and may be taken factor of water eutrophication. The third factor F3 accounting 17 % of the total variance and represents the influence of point sources of pollution on water quality. The fourth factor F4 accounting 13 % of the total variance and may be taken as an ecological dimension of water quality. Cluster analysis (CA) is an objective technique to identify natural groupings in the set of data. CA divides a large number of objects into smaller number of homogenous groups on the basis of their correlation structure. CA combines the data objects together to form the natural groups involving objects with similar cluster properties and separates the objects with different cluster properties. CA showed similarities and dissimilarities among the sampling sites and explain the observed clustering in terms of affected conditions. Using FA/PCA and CA have been identified water bodies that are under the highest pressure. With regard to the factors identified water bodies are: for factor F1 (Plazovic, Bosut, Studva, Zlatica, Stari Begej, Krivaja), for factor F2 (Krivaja, Keres), for factor F3 (Studva, Zlatica, Tamis, Krivaja i Keres) and for factor F4 (Studva, Zlatica, Krivaja, Keres).

scholarly journals Assessment of Tigris River Water Quality Using Multivariate Statistical Techniques

2019 ◽  
Vol 26 (4) ◽  
pp. 26-31
Author(s):  
Muntasir Shareef
Keyword(s):  
Water Quality ◽  
Factor Analysis ◽  
Total Variance ◽  
Quality Data ◽  
Statistical Techniques ◽  
Complex Nature ◽  
Multivariate Statistical Techniques ◽  
Multivariate Statistical ◽  
Water Quality Data ◽  
Tigris River

The present study uses the multivariate statistical techniques by applying the Factor Analysis (Principle component method) to explain the observed water quality data of Tigris river within Baghdad city. The water quality was analyzed at eleven different sites, along the river, over a period of one year (2017) using 20 water quality parameters. Five factors were identified by factor analysis which was responsible from the 72.291% of the total variance of the water quality in the Tigris river. The first factor called the pollution factor explained 34.387% of the total variance and the second factor called the surface runoff and erosion factor explained 11.875% of the total variance. While, the third, fourth, and fifth factors explained 10.213%, 8.861% and 6.956% of the total variance and called pH, Silica and nutrient factors, respectively. Multivariate statistical techniques can be effective methods to aid water resources managers understand complex nature of water quality issues and determine the priorities to sustain water quality.

Water quality assessment using multivariate statistical techniques in Río Tercero Reservoir, Argentina

2014 ◽  
Vol 46 (3) ◽  
pp. 377-388 ◽  
Author(s):  
Matias Bonansea ◽  
Claudia Ledesma ◽  
Claudia Rodriguez ◽  
Lucio Pinotti
Keyword(s):  
Water Quality ◽  
Quality Data ◽  
Spatial And Temporal Variation ◽  
Multidimensional Data ◽  
Statistical Techniques ◽  
Data Sets ◽  
Multivariate Statistical Techniques ◽  
Multivariate Statistical ◽  
Water Quality Data ◽  
Data Set

Water quality monitoring programs generate complex multidimensional data sets. In this study, multivariate statistical techniques were employed as an effective tool for the analysis and interpretation of these water quality data sets. Principal component analysis (PCA) and cluster analysis (CA) were applied to evaluate spatial and temporal variation of water quality in Río Tercero Reservoir (Argentina). Six sampling sites were surveyed each climatic season for 21 parameters during 2003–2010. The results revealed that PCA showed the existence of four significant principal components (PCs) which account for 96.7% of the total variance of the data set. The first PC was assigned to mineralization whereas the other PCs were built from variables indicative of pollution. Hierarchical CA grouped the six monitoring sites into three clusters and classified the different climatic seasons into two clusters based on similarities in water quality characteristics.

scholarly journals Evaluation of the anthropogenic impact in Suat Ugurlu Dam lake using multivariate statistical techniques

2018 ◽  
Vol 20 (1) ◽  
pp. 161-168 ◽  
Keyword(s):  
Anthropogenic Impact ◽  
Sediment Quality ◽  
Principal Component ◽  
Statistical Techniques ◽  
Multivariate Statistical Techniques ◽  
Cluster Number ◽  
Multivariate Statistical ◽  
Oxidation Reduction ◽  
Oxidation Reduction Potential ◽  
And Cluster Analysis

Sediments play an important role in the quality of aquatic ecosystems in the Dam Lake where they can either be a sink or a source of contaminants, depending on the management. This purpose of this study is to identify the sediment quality in order to find out the causes for the malodor and the eutrophication that is causing a bad scenario. Solutions for improving the dam are proposed. Multivariate statistical techniques, such as a principal component analysis (PCA) and cluster analysis (CA), were applied to the data regarding sediment quality in relation to anthropogenic impact in Suat Ugurlu Dam Lake. This data was generated during 2014-2015, with monitoring at four sites for 11 parameters. A PCA and CA were used in the study of the samples. The total variance of 84.1%, 74.3%, 87.4% and 91.5% suggest 4, 3, 3 and 4 principle components (PCs) in the four locations: LC1, LC2, LC3 and LC4, respectively. Also, a CA was applied to both the variables and the observations. Some variables and observations showed a high similarity based on the results of variables in the CA. Also, the similarity ratio of temperature-mercury (Hg) and oxidation reduction potential (ORP) was high and generally, the cluster number of variables was 5, according to the selected similarity level.

scholarly journals Countrywide Groundwater Quality Trend and Suitability for Use in Key Sectors of Korea

Water ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 1193
Author(s):  
Chanhyeok Jeon ◽  
Maimoona Raza ◽  
Jin-Yong Lee ◽  
Heejung Kim ◽  
Chang-Seong Kim ◽  
...  
Keyword(s):  
Groundwater Quality ◽  
Monitoring Network ◽  
Temporal Variations ◽  
World Health ◽  
Quality Data ◽  
Water Type ◽  
Suitability Analysis ◽  
Water Quality Data ◽  
Suitability Evaluation ◽  
Groundwater Samples

Under changing climate, increasing groundwater use has risen the concern for groundwater quality variations over recent years, to maintain a healthy ecosystem. The objectives were to identify trend of temporal variations in groundwater quality and its suitability for different uses in Republic of Korea. Water quality data were collected from 198 monitoring stations of Groundwater Quality Monitoring Network (GQMN), annually for the period of ten years (2008–2017). Non-parametric trend analysis of a Mann–Kendall test and Theil–Sen’s slope was done on groundwater physico-chemical data of ten years. Groundwater suitability evaluation was done for use in main sectors including domestic (drinking) and agriculture (irrigation). For drinking suitability analysis, results were compared with World Health Organization (WHO) and Korean Ministry of Environment (KME) established guidelines. For irrigation suitability evaluation, electrical conductivity (EC), Sodium Adsorption Ratio (SAR), percent of Na+, Residual Sodium Carbonate (RSC), US Salinity Laboratory (USSL), and Wilcox diagram were used. Most significantly, water type belongs to Ca-HCO3 and Ca-SO4 types, but a small proportion belongs to Na-CO3 and Na-Cl types. Approximately, 96% and 93% of groundwater samples are suitable for drinking, based on WHO and KME guidelines, respectively. Around 98% and 83% of groundwater samples are in suitable range for irrigation use, based on USSL and Wilcox diagrams, respectively.

scholarly journals Using multivariate statistical techniques to assess water quality of Nhu Y river in Thua Thien Hue province

2014 ◽  
Vol 17 (2) ◽  
pp. 50-60
Author(s):  
Ky Minh Nguyen ◽  
Lam Hoang Nguyen
Keyword(s):  
Water Quality ◽  
Correlation Analysis ◽  
Oxygen Demand ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Total Variance ◽  
Quality Data ◽  
Statistical Techniques ◽  
Multivariate Statistical Techniques ◽  
Multivariate Statistical

The aims of this research are to assess water quality by organic and nutrient matters and identifying the environmental pressures, examine the impact of the loads to Nhu Y River, Thua Thien-Hue Province. Five stations were sampled at Nhu Y River, the research had monitoring of water quality parameters such as Temperature (Temp), Dissolved Oxygen (DO), Biological Oxygen Demand (BOD5), Chemical Oxygen Demand (COD), Nitrate (NO3-) and Phosphate (PO43-). The research used multivariate statistical techniques such as correlation analysis, principal component analysis (PCA) and cluster analysis (CA) to assess water quality. The correlation analysis shown a strong positive correlation exists between water quality parameters such as TempDO and BOD5COD (p<0.01). The PCA technique was applied to water quality data sets, which was obtained from Nhu Y River and the results show that the indices which has changed water quality. The results of the PCA using a varimax rotation technique were illustrated with two principal components (PC) and accounts for 62.207% of the overall total variance. The first PC accounted for 40.873% of the total variance, which was loaded with Temp, DO, BOD5 and COD. The second PC consists of NO3- and PO43- which accounts for 21.334% of the total variance, it can be due to the discharge of agricultural activities. Similarly, the CA has identified two major clusters involving: BOD5, COD, Temp, DO (the first cluster) and NO3-, PO43- (the second cluster).

scholarly journals Seasonal Source Apportionment of Heavy Metals and Physicochemical Parameters: A Case Study of Sapanca Lake Watershed

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Asude Ateş ◽  
Hülya Demirel ◽  
Rabia Köklü ◽  
Şenay Çetin Doğruparmak ◽  
Hüseyin Altundağ ◽  
...  
Keyword(s):  
Toxic Metals ◽  
Physicochemical Parameters ◽  
Principal Component ◽  
Pollution Sources ◽  
Statistical Techniques ◽  
Natural Soil ◽  
Multivariate Statistical Techniques ◽  
Multivariate Statistical ◽  
Pollutant Sources ◽  
Mlr Model

This study was aimed to evaluate the water quality and pollution sources in Sapanca Lake and its tributaries by applying multivariate statistical techniques to physicochemical parameters and toxic metals. For this purpose, the multivariate statistical methods such as principal component analysis (PCA) and absolute principal component score-multiple linear regression (APCS-MLR) model have been employed. It was tried to determine the seasonal pollution sources of physicochemical parameters and toxic metals obtained from 22 different sampling points between the years of 2015 and 2017. PCA was applied to the datasets, and 6 varimax factors describing 84%, 80%, 76%, and 79% of the total variance for each season were extracted. The obtained factors were analyzed using the APCS-MLR model for the apportionment of various pollution sources affecting physicochemical parameters and toxic metals. The results show that the natural soil structure, municipal-industrial wastewater, agricultural-atmospheric runoff, highways, and seasonal effects are the major pollution sources for toxic metals and physicochemical parameters. The material contribution of pollutant sources to toxic metals and physicochemical parameters was calculated and verified by the concentrations analyzed. Consequently, multivariate statistical techniques are useful to determine the physicochemical parameters and toxic metals through reciprocal correlation and assess the seasonal impact of pollutant sources in the basin. This study also provides a basis for the creation of measurement programs, determination of pollution sources, and provision of sustainable watershed management regarding other water resources.

scholarly journals Water Quality Assessment of Porsuk River, Turkey

2010 ◽  
Vol 7 (2) ◽  
pp. 593-599 ◽  
Author(s):  
Suheyla Yerel
Keyword(s):  
Water Quality ◽  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Surface Water ◽  
Surface Water Quality ◽  
Principal Component ◽  
Statistical Techniques ◽  
Multivariate Statistical Techniques ◽  
Multivariate Statistical ◽  
Porsuk River

The surface water quality of Porsuk River in Turkey was evaluated by using the multivariate statistical techniques including principal component analysis, factor analysis and cluster analysis. When principal component analysis and factor analysis as applied to the surface water quality data obtain from the eleven different observation stations, three factors were determined, which were responsible from the 66.88% of total variance of the surface water quality in Porsuk River. Cluster analysis grouped eleven observation stations into two clusters under the similarity of surface water quality parameters. Based on the locations of the observation stations and variable concentrations at these stations, it was concluded that urban, industrial and agricultural discharge strongly affected east part of the region. Finally, this study shows that the usefulness of multivariate statistical techniques for analysis and interpretation of datasets and determination pollution factors for river water quality management.

scholarly journals SPATIAL AND TEMPORAL ASSESSMENT OF WATER QUALITY DATA USING MULTIVARIATE STATISTICAL TECHNIQUES

2016 ◽  
Vol 38 (2) ◽  
pp. 577
Author(s):  
Nícolas Reinaldo Finkler ◽  
Taison Anderson Bortolin ◽  
Jardel Cocconi ◽  
Ludmilson Abritta Mendes ◽  
Vania Elisabete Schneider
Keyword(s):  
Water Quality ◽  
Anthropogenic Activities ◽  
Principal Component ◽  
Quality Data ◽  
Spatial And Temporal Variation ◽  
Multivariate Statistical ◽  
Water Quality Data ◽  
And Cluster Analysis ◽  
Quality Variation ◽  
Human Density

The natural factors and anthropogenic activities that contribute to spatial and temporal variation in superficial waters in Caxias do Sul’s urban hydrographic basins were determined applying multivariate analysis of data. The techniques used in this study were Principal Component Analysis and Cluster Analysis. The monitoring was executed in 12 sampling stations, during January, 2009 to January, 2010 with monthly periodicity in total of 13 campaigns. Between chemical, biological and physical, 20 parameters were analyzed. The results state that with the use of ACP, a data variance of 70.94% was observed. Therefore, it testifies that major pollutants that contribute to a water quality variation in the county are classified as domestic and industrial pollutants, mainly from galvanic industry. Moreover, two clusters were found which differentiated regarding their location and distance from areas with a high human density, corroborating on identifying of impact due to human activities in urban rivers.

THE CONVERGENCE OF HABITAT STRUCTURE IN TUNDRA COLLEMBOLAN COMMUNITIES (INSECTA: ORDER COLLEMBOLA)

1983 ◽  
Vol 115 (9) ◽  
pp. 1129-1145 ◽  
Author(s):  
Masanori J. Toda ◽  
Kouzou Tanno
Keyword(s):  
Habitat Structure ◽  
Principal Component ◽  
The Other ◽  
Statistical Techniques ◽  
Mackenzie Delta ◽  
Multivariate Statistical Techniques ◽  
Multivariate Statistical ◽  
Cluster Analyses ◽  
Ecological Changes ◽  
Principal Component Analyses

AbstractHabitat structure of two collembolan communities, one at Barrow, Alaska, U.S.A., the other at Tuktoyaktuk in the Mackenzie Delta, Canada, has been analyzed in relation to microtopographies characteristic of tundra regions. Multivariate statistical techniques, cluster analyses (UPGMA), and principal component analyses (PCA) reveal various ecological changes in component species. In spite of such local variations in component species, the two communities show similar patterns of habitat structure that are organized principally along a gradient of environmental moisture.

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